This is the development site for the Modelica INTERSTORES library and its user guide.
The library is developed as part of the European project INTERSTORES
This library supports researchers in simulation of district heating and cooling systems integrated with Underground Thermal Energy Storage (UTES) technologies. The primary focus is on:
- Tank Thermal Energy Storage (TTES),
- Pit Thermal Energy Storage (PTES),
In addition to storage modeling, the library provides a wide range of configurable heat supply systems including:
- Waste heat from data centers,
- Industrial waste heat,
- Geothermal sources,
- Combined Heat and Power (CHP) units,
- Peak load boilers,
The demand side is also represented allowing users to define varying characteristics such as:
- Supply and return temperature levels,
- Pressure conditions,
- Fixed or dynamic return temperatures,
The coupling between the district heating network and end users is modeled through substations, which are fully integrated within the library framework. Most components are based on base classes developed in previous research projects and are further extended and adapted specifically for district-scale simulations incorporating UTES systems.
- To clone the repository for the first time run:
git clone https://github.com/AIT-TES/INTERSTORES.git
Until first stable release, you can checkout the source code from the main branch
The INTERSTORES Library is released by AIT Austrian Insitutue of Technology GmbH, Center for Energy, Sustainable Thermal Energy Systems and is available under a 3-clause BSD-license.
The library is currently in an early development stage, and additional features and enhancements are under active consideration. Issues can be reported using this site's Issues section. You are welcome to contribute via Pull Requests.
The project is financially supported by the EU Horizon Europe project INTERSTORES (project No. 101136100).Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or Research Executive Agency. Neither the European Union nor the granting authority can be held responsible for them.
To cite the library, use
Bayer, Michael, Meister, Curtis, Schuetz, Philipp et al., ‘Development of a reduced-order dynamic model for large-scale seasonal thermal energy storage applications’, Energy, 333 (2025), 137379.
@article{BAYER2025137379,
title = {Development of a reduced-order dynamic model for large-scale seasonal thermal energy storage applications},
journal = {Energy},
volume = {333},
pages = {137379},
year = {2025},
issn = {0360-5442},
doi = {https://doi.org/10.1016/j.energy.2025.137379},
url = {https://www.sciencedirect.com/science/article/pii/S036054422503021X},
author = {Michael Bayer and Curtis Meister and Philipp Schuetz and Willy Villasmil and Heimo Walter and Abdulrahman Dahash},
keywords = {Reduced-order model, Seasonal thermal energy storage, pit thermal energy storage, Modelica model, DePlaTES COMSOL, Energy system simulations},
abstract = {This study introduces an efficient simulation model for large-scale pit seasonal thermal energy storage (PTES) applications, designed to retain accuracy while significantly reducing computational demands. Being implemented in Modelica/Dymola, the reduced-order model is compared against an experimentally validated COMSOL Multiphysics simulation model based on key performance indicators including energy balance, thermal losses, temperature stratification and computational time. Energy balances of both models show good agreement, with deviations of less than 6 % in terms of charged energy and under 5 % in discharged energy. Total thermal losses align closely, with discrepancy below 2 %, underscoring the model's reliability. Temperature stratification analysis reveals strong alignment of both models under idle conditions, especially in the upper layers of the storage. During dynamic charging and discharging phases, minor discrepancies are observed, with root mean square error values ranging from 1.2 K in the upper layers to 2.4 K at the bottom. Additionally, the reduced-order model demonstrates a substantial reduction in computational time, making it up to 98 % faster than the COMSOL model. The model is therefore established as a highly efficient yet accurate tool for large-scale sTES simulations, particularly suited for iterative system design, optimization processes, and real-time control.}
}